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Re: st: Interpretation of Curvilinear Effects


From   Maarten buis <maartenbuis@yahoo.co.uk>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: Interpretation of Curvilinear Effects
Date   Wed, 10 Jun 2009 09:42:44 +0000 (GMT)

--- On Tue, 9/6/09, John Antonakis wrote:
> The best way to get a feel for the shape of the interaction
> is to plot it; i.e., fit the model, and then plug the
> numbers across your x-values and plot the predicted value.
> In the case of a positive x and positive x^2 the line should
> be relatively flat and positive and the shoot up like a "J"
> shape.
> 
> Or try this after you fit the model:
> 
> predictnl y_hat= 1.89 + _b[x]*x + _b[x^2]*x^2 ,
> ci(yhat_left yhat_right)
> twoway (connect y_hat  yhat_left yhat_right x, sort)
> 
> Instead of 1.89 above, put in the estimate of your
> intercept.

An alternative and somewhat more flexible way of including 
curvilinear effects is use restricted cubic splines. The 
-postrcspline- package available from SSC automates the 
plotting of the regression curve John proposed above. 
Moreover, a curvilinear effect implies that the effect of 
x changes over x. The -postrcspline- package also allows 
one to plot effect (first derivative) of x agains x. To 
install type in Stata: -ssc desc postrcspline-.

Hope this helps,
Maarten

-----------------------------------------
Maarten L. Buis
Institut fuer Soziologie
Universitaet Tuebingen
Wilhelmstrasse 36
72074 Tuebingen
Germany

http://home.fsw.vu.nl/m.buis/
-----------------------------------------


      

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